In the conventional art, a spam detection system analyzes the content of an electronic mail (“email”) messages and determines if the email message is unsolicited electronic junk email, also known as “spam”, or a legitimate email message. There are different approaches that are well-known in the art to accomplish this task, but none of them are able to work 100% error free. Common spam detection approaches include bayesian classifiers, artificial neuronal networks, and heuristic methods by looking at email headers or searching for typical spam patterns.
For some email messages, the spam detection system will identify the message as spam although the message was not spam. In this instance, it is said that the spam detection system has produced a “false positive.” When, in response to a “false positive,” a spam detection system blocks the email message, the system suffers from the problem of “overblocking.”
In view of the forgoing, there is a need in the art for a spam detection system that reduces the frequency of false positives and overblocking.